ABSTRACT Structural birth defects (SBDs) impact millions of children in the US and around the world every year. The Gabriella Miller Kids First (GMKF) program and other initiatives have committed significant resources toward understanding the genetic basis of these conditions, yet the genetic etiology and underlying pathogenic mechanisms remain unknown for most children that present with SBDs. This application will focus on two classes of genomic variation that likely contribute to this unexplained etiology in SBDs: structural variation (SV) and noncoding regulatory variation. Broadly defined as variants >50bp in size, SVs have been discovered to be far more prevalent and diverse in each human genome than previously appreciated. In this proposal, we will use our recently developed SV detection algorithms from whole-genome sequencing across GMKF SBD cohorts, and compare these findings to large population-scale datasets in the genome aggregation database (gnomAD-SV). In addition to variant detection and characterization, we will pursue disease association analyses in SBD cohorts. Given that most new (de novo) mutations that occur in children reside outside of the coding sequence of genes, it is imperative that models of disease association account for both coding and noncoding variation. Here, we will leverage recently developed analytic frameworks of ?category-wide association study? (CWAS) and de novo risk score approaches to perform association tests across all coding and noncoding single nucleotide variants, indels and structural variants in GMKF. Finally, we will integrate genome and exome findings from cases with neurodevelopmental disorders (NDD) to investigate commonalities in genes and pathways associated with SBDs and NDDs. Taken together, we expect these analyses to develop a comprehensive resource to interrogate the genetic landscape of SV and noncoding associations across SBDs in GMKF families and population controls.